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Embodied agents can now reliably achieve complex, multi-stage goals in dynamic environments by learning to predict and ground targets from decoupled visual context, even when the goal is out of view.
Hierarchical procedural understanding unlocks a 9.6%p performance boost in proactive AI assistance, enabling adaptive guidance that outperforms in-context learning by 57.1%p.
Rubric-based RL can be misled by "Vacuous Credit," but MetaRubric's adaptive rubric learning achieves substantial accuracy gains on complex QA tasks.
